Massive Multiple Input Multiple Output (M-MIMO) systems specifically refers to a practical technique for sending and receiving more than one data signal simultaneously over the same radio channel by exploiting multipath propagation". It depends on several antennas for transferring varied data streams simultaneously. With the increase in count of antennas, the energy or power utilization also gets increased. Thus, it becomes necessary to select optimal transmit antennas that exist as the great challenge in M-MIMO systems. This work introduces a new "Hybrid Sea Lion-Whale Algorithm (HS-WA)" for selecting the optimal transmit antenna by considering the multi-objectives, which increases both capacity and efficiency. The adopted scheme is the combination of both "Whale Optimization Algorithm (WOA) and Sea Lion Optimization Algorithm (SLnO)" that optimizes the antenna's count and moreover, it finds out "which antenna to be selected". At last, the supremacy of presented model is confirmed over existing models in terms EE and capacity analysis.
The massive Multiple-Input Multiple-Output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and Transmit Antenna Selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA as well as the TAS of wireless communication in millimetre- wave (mm-wave) with massive MIMO technology is considered. Two different solutions are developed for this research such as the Deep Learning method for efficient resource allocation process and optimization algorithm for Transmit Antenna Selection (TAS) process. Here, the RA process is done with the help of Attention Based Capsule Auto-Encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, Battle Royale Optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipments (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances Spectral Efficiency (SE), Energy Efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE and SE.
Ad hoc networks are autonomous and infrastructure-less wireless systems where nodes act as routers and hosts. Security is the primary issue for the functionality of these networks. Security for ad hoc networks can be incorporated by prevention and detection mechanisms. This research work focuses on a two-level fuzzy-based intrusion detection system for identifying black hole attacks in ad hoc networks. This method can reduce the complexity of the rule base of the fuzzy inference system. To reduce the complexity of detection, communication overhead and to make the detection scheme energy efficient, further, a cluster-head-based intrusion detection system is designed and implemented. The impact on network performance with no attack, with black hole attack, and with intrusion detection scheme deployed in all nodes and cluster heads are analyzed. The proposed cluster-based 2 level fuzzy logic intrusion detection mechanism was able to achieve the detection rate and accuracy to a maximum of 100%,false alarm rate to 0% and detection delay to in varying attacker scenario.
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